Next, it is obvious that the feature sharing strategy improves the performance of both semantic segmentation and depth estimation. For the example in the forth scene of REF , the baseline fails to predict the wall behind fridge while FSM help perceive the existence of these two objects and make boundaries in the depth map clearer. These improvements are facilitated by deeply fusing the task specific features, providing more robust information for prediction. In addition, we replace the FSM with LSU [1]}[2]} whose results can be seen in  REF . It is noticed that Although the LSU does improve the performance, the improvements are not as obvious as those by adding FSM, which proves the effectiveness of sampling operations.
